At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …

Convolutional neural networks depend on deep network architectures to extract accurate
information for image super‐resolution. However, obtained information of these …

Natural images tend to recur similar patterns within the same scale and across different
scales. Some recent progress on Single Image Super-Resolution (SISR) have elaborated on …

In image super-resolution task, the existing convolutional neural network methods proceed
to increase the number of network layers and filters to achieve better performance …

Experts require large high-resolution retinal images to detect tiny abnormalities, such as
microaneurysms or issues of vascular branches. However, these images often suffer from …

Recently, the quality of generated images in image super-resolution (SR) has significantly
improved due to the widespread application of convolutional neural networks. Existing super …

At present, artificial neural networks have received wide applications in the field of image
processing and image resolution because of their fast algorithm implementation and their …

In single image super resolution tasks, distortion measurement (such as PSNR, SSIM) and
perceptual quality (such as PI, NIQE) are contradictory, and methods that perform well in …

Removing texture while preserving the main structure of an image is a challenging task. To
address this, this paper propose an image smoothing method based on global gradient …

Multispectral image (MSI) reconstruction via remote sensing image fusion (RSIF), involving
the fusion of MSIs with panchromatic (PAN) images, has attracted considerable attention …